






"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_							      = logging.get_logger(__name__)


lowerCamelCase_							      = {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCamelCase_							      = {
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

lowerCamelCase_							      = {'''facebook/blenderbot_small-90M''': 512}

def 							snake_case (  A__		):
			UpperCAmelCase_	:					str     	=   set()
			UpperCAmelCase_	:					int     	=   word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)		)
						UpperCAmelCase_	:					Dict     	=   char

			UpperCAmelCase_	:					List[Any]     	=   set(A__		)
			return pairs







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']





	def __init__( self   :					Optional[int]      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Optional[int]="__start__"      ,      lowerCAmelCase_   :					str="__end__"      ,      lowerCAmelCase_   :					Union[str, Any]="__unk__"      ,      lowerCAmelCase_   :					Union[str, Any]="__null__"      ,      **lowerCAmelCase_   :					List[Any]      ,      )		->     str:
				super().__init__(unk_token=lowerCAmelCase_      ,      bos_token=lowerCAmelCase_      ,      eos_token=lowerCAmelCase_      ,      pad_token=lowerCAmelCase_      ,      **lowerCAmelCase_		)

				with open(lowerCAmelCase_      ,      encoding="utf-8"		) as vocab_handle:
							UpperCAmelCase_	:					str     	=   json.load(lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   {v: k for k, v in self.encoder.items()}
				with open(lowerCAmelCase_      ,      encoding="utf-8"		) as merges_handle:
							UpperCAmelCase_	:					Optional[Any]     	=   merges_handle.read().split("\n"		)[1:-1]
				UpperCAmelCase_	:					str     	=   [tuple(merge.split()		) for merge in merges]
				UpperCAmelCase_	:					Any     	=   dict(zip(lowerCAmelCase_      ,      range(len(lowerCAmelCase_		)		)		)		)
				UpperCAmelCase_	:					Any     	=   {}





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				return len(self.encoder		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Dict:
				return dict(self.encoder      ,      **self.added_tokens_encoder		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					str		)		->     str:
				if token in self.cache:
							return self.cache[token]
				UpperCAmelCase_	:					Dict     	=   re.sub("([.,!?()])"      ,      R" \1"      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   re.sub("(')"      ,      R" \1 "      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   re.sub(R"\s{2,}"      ,      " "      ,      lowerCAmelCase_		)
				if "\n" in token:
							UpperCAmelCase_	:					Tuple     	=   token.replace("\n"      ,      " __newln__"		)

				UpperCAmelCase_	:					Tuple     	=   token.split(" "		)
				UpperCAmelCase_	:					int     	=   []
				for token in tokens:
							if not len(lowerCAmelCase_		):
										continue

							UpperCAmelCase_	:					Any     	=   token.lower()
							UpperCAmelCase_	:					List[str]     	=   tuple(lowerCAmelCase_		)
							UpperCAmelCase_	:					List[str]     	=   tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
							UpperCAmelCase_	:					List[Any]     	=   get_pairs(lowerCAmelCase_		)

							if not pairs:
										words.append(lowerCAmelCase_		)
										continue

							while True:
										UpperCAmelCase_	:					Union[str, Any]     	=   min(lowerCAmelCase_      ,      key=lambda lowerCAmelCase_		: self.bpe_ranks.get(lowerCAmelCase_      ,      float("inf"		)		)		)
										if bigram not in self.bpe_ranks:
													break
										UpperCAmelCase_	,      UpperCAmelCase_	:					Dict     	=   bigram
										UpperCAmelCase_	:					Dict     	=   []
										UpperCAmelCase_	:					Dict     	=   0

										while i < len(lowerCAmelCase_		):
													try:
																UpperCAmelCase_	:					int     	=   word.index(lowerCAmelCase_      ,      lowerCAmelCase_		)
																new_word.extend(word[i:j]		)
																UpperCAmelCase_	:					Any     	=   j
													except ValueError:
																new_word.extend(word[i:]		)
																break

													if word[i] == first and i < len(lowerCAmelCase_		) - 1 and word[i + 1] == second:
																new_word.append(first + second		)
																i += 2
													else:
																new_word.append(word[i]		)
																i += 1
										UpperCAmelCase_	:					Dict     	=   tuple(lowerCAmelCase_		)
										UpperCAmelCase_	:					Any     	=   new_word
										if len(lowerCAmelCase_		) == 1:
													break
										else:
													UpperCAmelCase_	:					Union[str, Any]     	=   get_pairs(lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[int]     	=   "@@ ".join(lowerCAmelCase_		)
							UpperCAmelCase_	:					List[str]     	=   word[:-4]

							UpperCAmelCase_	:					Optional[Any]     	=   word
							words.append(lowerCAmelCase_		)
				return " ".join(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					str		)		->     List[str]:
				UpperCAmelCase_	:					Union[str, Any]     	=   []

				UpperCAmelCase_	:					List[Any]     	=   re.findall(R"\S+\n?"      ,      lowerCAmelCase_		)

				for token in words:
							split_tokens.extend(list(self.bpe(lowerCAmelCase_		).split(" "		)		)		)
				return split_tokens





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					str		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   token.lower()
				return self.encoder.get(lowerCAmelCase_      ,      self.encoder.get(self.unk_token		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					int		)		->     str:
				return self.decoder.get(lowerCAmelCase_      ,      self.unk_token		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					List[str]		)		->     str:
				UpperCAmelCase_	:					List[str]     	=   " ".join(lowerCAmelCase_		).replace("@@ "      ,      ""		).strip()
				return out_string





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     Tuple[str]:
				if not os.path.isdir(lowerCAmelCase_		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_	:					Dict     	=   os.path.join(
				    lowerCAmelCase_      ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
				UpperCAmelCase_	:					Union[str, Any]     	=   os.path.join(
				    lowerCAmelCase_      ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

				with open(lowerCAmelCase_      ,      "w"      ,      encoding="utf-8"		) as f:
							f.write(json.dumps(self.encoder      ,      indent=2      ,      sort_keys=lowerCAmelCase_      ,      ensure_ascii=lowerCAmelCase_		) + "\n"		)

				UpperCAmelCase_	:					Any     	=   0
				with open(lowerCAmelCase_      ,      "w"      ,      encoding="utf-8"		) as writer:
							writer.write("#version: 0.2\n"		)
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,      key=lambda lowerCAmelCase_		: kv[1]		):
										if index != token_index:
													logger.warning(
													    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
													    " Please check that the tokenizer is not corrupted!"		)
													UpperCAmelCase_	:					List[Any]     	=   token_index
										writer.write(" ".join(lowerCAmelCase_		) + "\n"		)
										index += 1

				return vocab_file, merge_file





"""simple docstring"""



import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = OmegaConf.load(_A   )
    if display:
        print(yaml.dump(OmegaConf.to_container(_A   )   )   )
    return config


def 			_UpperCamelCase						(    _A       ,   _A=None       ,   _A=None   )     ->	List[Any]:





    """simple docstring"""

    if conf_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.yaml"""
    _UpperCAmelCase        = load_config(_A       ,   display=_A   )
    _UpperCAmelCase        = VQModel(**config.model.params   )
    if ckpt_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.pt"""
    _UpperCAmelCase        = torch.load(_A       ,   map_location=_A   )
    if ".ckpt" in ckpt_path:
        _UpperCAmelCase        = sd["""state_dict"""]
    model.load_state_dict(_A       ,   strict=_A   )
    model.to(_A   )
    del sd
    return model


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = model.encode(_A   )
    print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""   )
    _UpperCAmelCase        = model.decode(_A   )
    return xrec


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = string.rsplit("""."""       ,   1   )
    if reload:
        _UpperCAmelCase        = importlib.import_module(_A   )
        importlib.reload(_A   )
    return getattr(importlib.import_module(_A       ,   package=_A   )       ,   cls   )


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    if "target" not in config:
        raise KeyError("""Expected key `target` to instantiate."""   )
    return get_obj_from_str(config["""target"""]   )(**config.get("""params"""       ,   {}   )   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=True       ,   _A=True   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = instantiate_from_config(_A   )
    if sd is not None:
        model.load_state_dict(_A   )
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    if ckpt:
        _UpperCAmelCase        = torch.load(_A       ,   map_location="""cpu"""   )
        _UpperCAmelCase        = pl_sd["""global_step"""]
        print(F"""loaded model from global step {global_step}."""   )
    else:
        _UpperCAmelCase        = {"""state_dict""": None}
        _UpperCAmelCase        = None
    _UpperCAmelCase        = load_model_from_config(config.model       ,   pl_sd["""state_dict"""]       ,   gpu=_A       ,   eval_mode=_A   )["""model"""]
    return model, global_step